Pathak Sayan D, Ng Lydia, Wyman Brad, Fogarasi Stephen, Racki Stephen, Oelund John C, Sparks Bobbi, Chalana Vikram
Insightful Corporation, 1700 Westlake Ave. N, Suite 500, Seattle, WA 98109, USA.
Drug Discov Today. 2003 May 15;8(10):451-8. doi: 10.1016/s1359-6446(03)02698-9.
Multi-dimensional image analysis is being used increasingly to arrive at surrogate end-points for drug development trials. Various imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound are used to analyze treatments for diseases such as cancer, multiple sclerosis, osteoarthritis, and Alzheimer's disease. However, extracting information from images can be tedious and is prone to high user variability. The medical image analysis community is moving towards advanced software systems specifically designed for drug development trials. These systems can automatically identify the anatomy of interest in medical images (segmentation methods), can compare the anatomy over time or between patients (registration methods) and allow the quantitative extraction of anatomical features and the integration of the data and results into a database management system, automatically tracking the changes made to the data (audit trail generation). In this article, we present a case study using a prototype system that is used for quantifying multiple sclerosis lesions from multivariate MRI.
